Economic Approaches to Improving Access to Evidence-Based and Recovery-Oriented Services for People with Severe Mental Illness
Bibliographic record
Abstract
During the past 3 decades, research has identified several psychosocial evidence-based practices (EBPs) for people with severe mental illness (SMI). Starting from a different origin, the recovery movement has influenced perceptions of how EBPs and other services should be delivered, and also emphasized the value of peer supports. We now know much more than 30 years ago about the kinds of services that help people with SMI live satisfying lives in the community. Evidence-based and recovery-oriented services require additional resources but use them sparingly: they are highly individualized, often result in reductions in costs of other mental health services, such as hospitalizations, and favour reliance on and integration into community settings rather than mental health services. Nevertheless, access to such services remains very limited. During the same period, the place of medications in the services system has become a source of growing concern, and there are several reasons to believe that current spending on medications is excessive. Inadequate housing and community supports that increase lengths of stay unnecessarily and spending on ineffective, nonrecovery-oriented vocational services are only 2 additional forms of misallocation of resources. Devolving control over medication budgets to regional or local health authorities, introducing program budgeting and marginal analysis, and implementing individual budgets to give more control to service users (in addition to promoting shared decision making) merit further investigation as potential strategies to improve outcomes for people with SMI in Canada in the context of limited budgets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".